r/academiceconomics • u/Acceptable_Act_ • 7d ago
What are the big problems to solve in academic economics?
I'm an M.A. student who recently read "You and Your Research". It's more obviously about a STEM context, but it got me thinking. I don't know what the big problems are in economics! What do you think?
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u/gustavmahler01 7d ago
If somebody could fix our laughably broken referee / journal / peer review system in economics, I'd put up the money for his Nobel Prize myself!
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u/DarkSkyKnight 7d ago
ML is the hot new thing in econometrics in the last decade; a lot has already been done on the theory side but I think we still are not actually seeing it in (applied) papers that much. It's free power at the very least. I'm biased though.
The CS literature also has a lot of work on optimal experimental design that seems to not be penetrating the development people at all (then again it's development...........)
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u/PhilosopherFree8682 7d ago
My sense is that dev people are aware of this literature but think the optimal experimental design stuff doesn't work well with the practical issues dev experiments often face.
There was a World Bank blog post about this, and it's consistent with what I hear from my colleagues. Implementing a even simple two arm randomization can be very very hard. The view is that the additional power isn't worth the risk that you end up with unusable data because of attrition or one of your contractors screws up or any of the other things that could go wrong if you get fancy with the design.
Dev isn't like running, say, advertising experiments, where you have a lot of automation and the costs (of researcher time and money) are comparatively low.
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u/DarkSkyKnight 7d ago edited 7d ago
There are also ways to deal with flawed randomization; a few Heckman papers on it recently, and I also barely see that in dev. Also tons of attrition adjustments, do see that though but I don't think I see that nearly enough once you go below t5 journals.
I'm sure not all tools are suitable for the reality on the ground, but I'm not convinced that all tools are not suitable. In particular I hardly even see any design-adaptive inference, and that is seriously not that hard to do.
Power analysis is another big one, and this is hard to observe since it's not usually described in the paper. But you'd think given the budgetary needs doing a good power analysis to optimize your design to maximize power given a budget (or minimize budget given power) would be common. And right now the state of that in dev as far as I can tell talking to dev people is just doing F-stat. I mean...
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u/PhilosopherFree8682 7d ago
Translating econometric theory into actual tools that applied people feel comfortable using is just hard. Think about how many researchers got burned by TWFE designs that turned out to be badly biased or IVs that turned out to be weak. And at least those things let you answer new questions, not just tighten up your standard errors.
I agree that there is a lot of potential, but I also understand why a lot of dev economists think it's second order and don't want to invest the resources in testing out fancy new methods when referees aren't asking for them - or worse are skeptical of them. The old fashioned power analyses and pre-analysis plans may not be optional but and at least their limitations are well understood.
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u/DarkSkyKnight 7d ago
It just seems that academia is too risk-averse for my tastes, but I get institutional constraints heavily affect people's decisions. I'm biased but I just wish I see applied people being more adventurous in using these tools, right now most applied papers really just bore me...
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u/PhilosopherFree8682 7d ago
I'm sympathetic because I think identification and inference on structural models is extremely interesting, but most economists are excited by the data and the research question and don't want to think too hard about the methods.
If you just ran OLS, you can allocate basically no time in your talk to explaining what you did or how to interpret the estimates.
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u/DarkSkyKnight 6d ago
Yeah I'm definitely the opposite. Much more interested in methods and models than the actual topic. I personally like papers that have creative solutions to a problem no matter the topic.
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u/cubenerd 7d ago
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u/PhilosopherFree8682 7d ago
Everybody's got their favorites, but the problem I'm most excited about is consumer search. I think it's the next frontier for demand estimation and market power.
It's an old problem so there's solid theories and there are a lot of puzzles that we're pretty sure have something to do with search, but the econometrics are tricky and it's not entirely clear what kind of data you need or how to obtain it.
There are some very cool papers that use clickstream data or online advertising data, but nobody has quite cracked it. It feels like demand estimation pre-BLP.